From fee7e8a2d9b8d0fd01c388e3102f9a4158fdf922 Mon Sep 17 00:00:00 2001
From: Lars Buitinck <L.J.Buitinck@uva.nl>
Date: Tue, 15 Nov 2011 23:46:36 +0100
Subject: [PATCH] FIX use super consistently in SVMs
---
sklearn/svm/classes.py | 36 +++++++++++++++++------------------
sklearn/svm/sparse/classes.py | 30 ++++++++++++++---------------
2 files changed, 33 insertions(+), 33 deletions(-)
diff --git a/sklearn/svm/classes.py b/sklearn/svm/classes.py
index 30ad3762c8..50b026e942 100644
--- a/sklearn/svm/classes.py
+++ b/sklearn/svm/classes.py
@@ -164,9 +164,9 @@ class SVC(DenseBaseLibSVM, ClassifierMixin):
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200):
- DenseBaseLibSVM.__init__(self, 'c_svc', kernel, degree, gamma, coef0,
- tol, C, 0., 0., shrinking, probability,
- cache_size)
+ super(SVC, self).__init__('c_svc', kernel, degree, gamma, coef0, tol,
+ C, 0., 0., shrinking, probability,
+ cache_size)
class NuSVC(DenseBaseLibSVM, ClassifierMixin):
@@ -269,9 +269,9 @@ class NuSVC(DenseBaseLibSVM, ClassifierMixin):
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200):
- DenseBaseLibSVM.__init__(self, 'nu_svc', kernel, degree, gamma,
- coef0, tol, 0., nu, 0., shrinking, probability,
- cache_size)
+ super(NuSVC, self).__init__('nu_svc', kernel, degree, gamma, coef0,
+ tol, 0., nu, 0., shrinking, probability,
+ cache_size)
class SVR(DenseBaseLibSVM, RegressorMixin):
@@ -359,9 +359,9 @@ class SVR(DenseBaseLibSVM, RegressorMixin):
tol=1e-3, C=1.0, epsilon=0.1, shrinking=True,
probability=False, cache_size=200):
- DenseBaseLibSVM.__init__(self, 'epsilon_svr', kernel, degree, gamma,
- coef0, tol, C, 0., epsilon, shrinking,
- probability, cache_size)
+ super(SVR, self).__init__('epsilon_svr', kernel, degree, gamma, coef0,
+ tol, C, 0., epsilon, shrinking, probability,
+ cache_size)
def fit(self, X, y, sample_weight=None, **params):
"""
@@ -384,8 +384,8 @@ class SVR(DenseBaseLibSVM, RegressorMixin):
Returns self.
"""
# we copy this method because SVR does not accept class_weight
- return DenseBaseLibSVM.fit(self, X, y, sample_weight=sample_weight,
- **params)
+ return super(SVR, self).fit(X, y, sample_weight=sample_weight,
+ **params)
class NuSVR(DenseBaseLibSVM, RegressorMixin):
@@ -473,9 +473,9 @@ class NuSVR(DenseBaseLibSVM, RegressorMixin):
gamma=0.0, coef0=0.0, shrinking=True,
probability=False, tol=1e-3, cache_size=200):
- DenseBaseLibSVM.__init__(self, 'nu_svr', kernel, degree, gamma, coef0,
- tol, 0, nu, None, shrinking, probability,
- cache_size)
+ super(NuSVR, self).__init__('nu_svr', kernel, degree, gamma, coef0,
+ tol, 0, nu, None, shrinking, probability,
+ cache_size)
def fit(self, X, y, sample_weight=None, **params):
"""
@@ -495,7 +495,7 @@ class NuSVR(DenseBaseLibSVM, RegressorMixin):
Returns self.
"""
# we copy this method because SVR does not accept class_weight
- return DenseBaseLibSVM.fit(self, X, y, sample_weight=[], **params)
+ return super(NuSVR, self).fit(X, y, sample_weight=[], **params)
class OneClassSVM(DenseBaseLibSVM):
@@ -557,9 +557,9 @@ class OneClassSVM(DenseBaseLibSVM):
"""
def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
tol=1e-3, nu=0.5, shrinking=True, cache_size=200):
- DenseBaseLibSVM.__init__(self, 'one_class', kernel, degree, gamma,
- coef0, tol, 0., nu, 0., shrinking, False,
- cache_size)
+ super(OneClassSVM, self).__init__('one_class', kernel, degree, gamma,
+ coef0, tol, 0., nu, 0., shrinking,
+ False, cache_size)
def fit(self, X, class_weight={}, sample_weight=None, **params):
"""
diff --git a/sklearn/svm/sparse/classes.py b/sklearn/svm/sparse/classes.py
index 5e3b2aef38..18b8f2a8ee 100644
--- a/sklearn/svm/sparse/classes.py
+++ b/sklearn/svm/sparse/classes.py
@@ -33,9 +33,9 @@ class SVC(SparseBaseLibSVM, ClassifierMixin):
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200):
- SparseBaseLibSVM.__init__(self, 'c_svc', kernel, degree, gamma, coef0,
- tol, C, 0., 0.,
- shrinking, probability, cache_size)
+ super(SVC, self).__init__('c_svc', kernel, degree, gamma, coef0, tol,
+ C, 0., 0., shrinking, probability,
+ cache_size)
class NuSVC(SparseBaseLibSVM, ClassifierMixin):
@@ -67,9 +67,9 @@ class NuSVC(SparseBaseLibSVM, ClassifierMixin):
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200):
- SparseBaseLibSVM.__init__(self, 'nu_svc', kernel, degree,
- gamma, coef0, tol, 0., nu, 0.,
- shrinking, probability, cache_size)
+ super(NuSVC, self).__init__('nu_svc', kernel, degree, gamma, coef0,
+ tol, 0., nu, 0., shrinking, probability,
+ cache_size)
class SVR(SparseBaseLibSVM, RegressorMixin):
@@ -101,9 +101,9 @@ class SVR(SparseBaseLibSVM, RegressorMixin):
tol=1e-3, C=1.0, epsilon=0.1,
shrinking=True, probability=False, cache_size=200):
- SparseBaseLibSVM.__init__(self, 'epsilon_svr', kernel,
- degree, gamma, coef0, tol, C, 0.0,
- epsilon, shrinking, probability, cache_size)
+ super(SVR, self).__init__('epsilon_svr', kernel, degree, gamma, coef0,
+ tol, C, 0.0, epsilon, shrinking, probability,
+ cache_size)
class NuSVR(SparseBaseLibSVM, RegressorMixin):
@@ -135,9 +135,9 @@ class NuSVR(SparseBaseLibSVM, RegressorMixin):
gamma=0.0, coef0=0.0, shrinking=True, epsilon=0.1,
probability=False, tol=1e-3, cache_size=200):
- SparseBaseLibSVM.__init__(self, 'nu_svr', kernel,
- degree, gamma, coef0, tol, 1.0, nu,
- epsilon, shrinking, probability, cache_size)
+ super(NuSVR, self).__init__('nu_svr', kernel, degree, gamma, coef0,
+ tol, 1.0, nu, epsilon, shrinking,
+ probability, cache_size)
class OneClassSVM(SparseBaseLibSVM):
@@ -156,9 +156,9 @@ class OneClassSVM(SparseBaseLibSVM):
tol=1e-3, nu=0.5, shrinking=True,
probability=False, cache_size=200):
- SparseBaseLibSVM.__init__(self, 'one_class', kernel, degree,
- gamma, coef0, tol, 0.0, nu, 0.0,
- shrinking, probability, cache_size)
+ super(OneClassSVM, self).__init__('one_class', kernel, degree, gamma,
+ coef0, tol, 0.0, nu, 0.0, shrinking,
+ probability, cache_size)
def fit(self, X, class_weight=None, sample_weight=None):
super(OneClassSVM, self).fit(
--
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